Mapping the financial sector in Africa
Mapping the financial sector in Africa
Can we track access to finance, such as savings, credit, money transfers or microinsurance, with maps that are as detailed and up-to-date as the maps we use in our day-to-day lives? More to the point, why would anyone want to do that?
At Microfinance Information Exchange (MIX) we are trying to answer these questions and have recently released maps of the financial sector in Kenya, Rwanda and South Africa as part of this investigation. These maps provide location information for thousands of points of service, combined with data on poverty, population and the demand for financial services. More on the ‘how’ these maps are built in a moment, but let’s cover the ‘why’ first.
Access to finance is a central area of focus for development practitioners. The World Bank has made a huge contribution to the knowledge base with the release of the Global Findex earlier in the year. But practitioners and policy-makers often need to know more about the dynamics of financial services providers within markets to identify gaps between supply and demand.
Maps are one way to do this by visualizing key data on access to finance. Policy-makers already track progress on financial inclusion by looking at the spatial distribution of services. Geospatial information can be used by commercial actors, policy-makers, regulators and donors. Research by the World Bank has shown that credit and savings use is connected with branch infrastructure and that new branches may spur entrepreneurial activity and job creation. Tracking local trends can also help to identify hotspots for risks and market concentration.
Using this as a motivation, we have been working to find and visualize geospatial data on financial inclusion in several countries. A few key principles have guided this work.
The first is to take public data seriously. In the long-run, we want more public data resources on access to finance. We believe that one way to improve public data is to actually use the existing public data, even if this ties our hands a bit.
To that end, we made a concerted effort to track down public data for each country. In South Africa, the National Credit Regulator has a comprehensive public database on all credit providers. In Rwanda, the Bank of Rwanda releases occasional updates on licensed MFIs and savings and credit cooperatives. We also referenced the branch listings posted for customers, on the assumption that these are likely to be a precise, complete and up-to-date source of locations.
This brings us to the second point: use technology to increase efficiency. Unfortunately, these resources are not always as accessible or easy-to-use as one might like. We had to extract data from regulatory databases and branch listings using web scrapers, all of which are publicly available. We relied on local experts to help clean and code location data. Our work with Development Seed has helped to make better tools available for developing maps quickly and easily, investments that should make it easier to replicate this work in the future.
Public data and technology have their limits, however. To go the last mile, we worked with data providers to make their hidden data public. For instance, while the Open Data Kenya platform has a vast range of data on the country, there is no data on financial services (yet). For our map of Kenya, we then worked with FSD Kenya and the Central Bank of Kenya to open access to data on bank branches. Data on branches and agents for the Kenya Post Office Savings Bank (KPOSB) came from KPOSB, geolocated by Reason & Skyll with support from World Savings Banks Institute (WSBI). Data on over 4,000 savings and credit cooperatives (SACCOs) were shared by the Kenya staff of the World Council of Credit Unions (WOCCU). Data on the location of M-Pesa mobile banking agents were pulled via web scraping. Listings for non-deposit-taking microfinance institutions come from the database of AMFI Kenya.
This is the first time most of this data has been publicly available. Brought together, they give us an unprecedented view on the financial landscape in Kenya. No one had objections to making this data public, it just took a nudge and a platform through which to open access to the data.
Hopefully this is the tip of the iceberg - the first steps to making higher quality data more readily accessible around the world. Regulators, associations, credit bureaus and researchers have worked on sector mapping in Bolivia, Ethiopia, India, Mexico, Morocco, Pakistan, Senegal, Sierra Leone, Thailand, Uganda, Uruguay and likely more. The more of this data is in the public eye, the more we can learn about the landscape of services available for the poor.
Scott Gaul is the Director of Analysis at MIX, which provides performance information on microfinance institutions (MFIs), funders, networks and service providers dedicated to serving the financial sector needs for low-income clients.